SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
SOLAR (Self-Optimizing Lifelong Autonomous Reasoner) is a new open-ended autonomous agent introduced in a paper on arXiv (2605.20189). It addresses concept drift and high cost of gradient-based adaptation in dynamic settings. SOLAR uses parameter-level meta-learning to treat model weights as an environment for exploration, consolidating a strong prior over common-sense knowledge for transfer learning. A multi-level reinforcement learning approach allows it to autonomously discover adaptation strategies for test-time adaptation to unseen domains. It maintains an evolving knowledge base of valid modification strategies, acting as an episodic memory buffer to prevent catastrophic forgetting. This approach eliminates the need for manual data curation and gradient-based fine-tuning, making it suitable for streaming and continual learning scenarios.
Enables LLMs to adapt continuously without costly retraining, reducing manual intervention.